def test_GBM_regression_rand2(self):
h2o.beta_features = True
bucket = 'home-0xdiag-datasets'
modelKey = 'GBMModelKey'
files = [
# ('standard', 'covtype.shuffled.90pct.data', 'covtype.train.hex', 1800, 54, 'covtype.shuffled.10pct.data', 'covtype.test.hex')
('standard', 'covtype.shuffled.10pct.sorted.data', 'covtype.train.hex', 1800, 'C54', 'covtype.shuffled.10pct.data', 'covtype.test.hex')
]
for (importFolderPath, trainFilename, trainKey, timeoutSecs, response, testFilename, testKey) in files:
# PARSE train****************************************
start = time.time()
xList = []
eList = []
fList = []
# Parse (train)****************************************
parseTrainResult = h2i.import_parse(bucket=bucket, path=importFolderPath + "/" + trainFilename, schema='local',
hex_key=trainKey, timeoutSecs=timeoutSecs, doSummary=False)
elapsed = time.time() - start
print "train parse end on ", trainFilename, 'took', elapsed, 'seconds',\
"%d pct. of timeout" % ((elapsed*100)/timeoutSecs)
print "train parse result:", trainKey
# Parse (test)****************************************
parseTestResult = h2i.import_parse(bucket=bucket, path=importFolderPath + "/" + testFilename, schema='local',
hex_key=testKey, timeoutSecs=timeoutSecs, doSummary=False)
elapsed = time.time() - start
print "test parse end on ", testFilename, 'took', elapsed, 'seconds',\
"%d pct. of timeout" % ((elapsed*100)/timeoutSecs)
print "test parse result:", testKey
paramsDict = define_gbm_params()
for trial in range(3):
# use this to set any defaults you want if the pick doesn't set
print "Regression!"
params = {'response': 'C54', 'ignored_cols_by_name': 'C5,C6,C7,C8,C9', 'ntrees': 2, 'classification': 0}
h2o_gbm.pickRandGbmParams(paramsDict, params)
print "Using these parameters for GBM: ", params
kwargs = params.copy()
# GBM train****************************************
trainStart = time.time()
gbmTrainResult = h2o_cmd.runGBM(parseResult=parseTrainResult,
timeoutSecs=timeoutSecs, destination_key=modelKey, **kwargs)
trainElapsed = time.time() - trainStart
print "GBM training completed in", trainElapsed, "seconds. On dataset: ", trainFilename
gbmTrainView = h2o_cmd.runGBMView(model_key=modelKey)
# errrs from end of list? is that the last tree?
errsLast = gbmTrainView['gbm_model']['errs'][-1]
print "GBM 'errsLast'", errsLast
# for regression, the cms are all null, so don't print
# GBM test****************************************
predictKey = 'Predict.hex'
start = time.time()
gbmTestResult = h2o_cmd.runPredict(
data_key=testKey,
model_key=modelKey,
destination_key=predictKey,
timeoutSecs=timeoutSecs)
elapsed = time.time() - start
print "GBM predict completed in", elapsed, "seconds. On dataset: ", testFilename
print "FIX! where do we get the summary info on the test data after predict?"
def test_GBM_regression_rand2(self):
bucket = 'home-0xdiag-datasets'
modelKey = 'GBMModelKey'
files = [
# ('standard', 'covtype.shuffled.90pct.data', 'covtype.train.hex', 1800, 54, 'covtype.shuffled.10pct.data', 'covtype.test.hex')
('standard', 'covtype.shuffled.10pct.sorted.data',
'covtype.train.hex', 1800, 'C55', 'covtype.shuffled.10pct.data',
'covtype.test.hex')
]
for (importFolderPath, trainFilename, trainKey, timeoutSecs, response,
testFilename, testKey) in files:
# PARSE train****************************************
start = time.time()
xList = []
eList = []
fList = []
# Parse (train)****************************************
parseTrainResult = h2i.import_parse(bucket=bucket,
path=importFolderPath + "/" +
trainFilename,
schema='local',
hex_key=trainKey,
timeoutSecs=timeoutSecs,
doSummary=False)
elapsed = time.time() - start
print "train parse end on ", trainFilename, 'took', elapsed, 'seconds',\
"%d pct. of timeout" % ((elapsed*100)/timeoutSecs)
print "train parse result:", trainKey
# Parse (test)****************************************
parseTestResult = h2i.import_parse(bucket=bucket,
path=importFolderPath + "/" +
testFilename,
schema='local',
hex_key=testKey,
timeoutSecs=timeoutSecs,
doSummary=False)
elapsed = time.time() - start
print "test parse end on ", testFilename, 'took', elapsed, 'seconds',\
"%d pct. of timeout" % ((elapsed*100)/timeoutSecs)
print "test parse result:", testKey
paramsDict = define_gbm_params()
for trial in range(3):
# use this to set any defaults you want if the pick doesn't set
print "Regression!"
params = {
'response': 'C55',
# 'ignored_cols_by_name': 'C5,C6,C7,C8,C9',
'ntrees': 2,
'classification': 0,
'validation': testKey,
}
h2o_gbm.pickRandGbmParams(paramsDict, params)
print "Using these parameters for GBM: ", params
kwargs = params.copy()
# GBM train****************************************
trainStart = time.time()
gbmTrainResult = h2o_cmd.runGBM(parseResult=parseTrainResult,
timeoutSecs=timeoutSecs,
destination_key=modelKey,
**kwargs)
trainElapsed = time.time() - trainStart
print "GBM training completed in", trainElapsed, "seconds. On dataset: ", trainFilename
gbmTrainView = h2o_cmd.runGBMView(model_key=modelKey)
print "gbmTrainView:", h2o.dump_json(gbmTrainView)
# errrs from end of list? is that the last tree?
errsLast = gbmTrainView['gbm_model']['errs'][-1]
print "GBM 'errsLast'", errsLast
# for regression, the cms are all null, so don't print
# GBM test****************************************
predictKey = 'Predict.hex'
start = time.time()
gbmTestResult = h2o_cmd.runPredict(data_key=testKey,
model_key=modelKey,
destination_key=predictKey,
timeoutSecs=timeoutSecs)
elapsed = time.time() - start
print "GBM predict completed in", elapsed, "seconds. On dataset: ", testFilename
print "FIX! where do we get the summary info on the test data after predict?"
def test_GBM_params_rand2(self):
bucket = 'home-0xdiag-datasets'
modelKey = 'GBMModelKey'
files = [
# ('standard', 'covtype.shuffled.90pct.data', 'covtype.train.hex', 1800, 54, 'covtype.shuffled.10pct.data', 'covtype.test.hex')
('standard', 'covtype.shuffled.10pct.sorted.data',
'covtype.train.hex', 1800, 54, 'covtype.shuffled.10pct.data',
'covtype.test.hex')
]
for (importFolderPath, trainFilename, trainKey, timeoutSecs, response,
testFilename, testKey) in files:
# PARSE train****************************************
start = time.time()
xList = []
eList = []
fList = []
# Parse (train)****************************************
parseTrainResult = h2i.import_parse(bucket=bucket,
path=importFolderPath + "/" +
trainFilename,
schema='local',
hex_key=trainKey,
timeoutSecs=timeoutSecs,
doSummary=False)
elapsed = time.time() - start
print "train parse end on ", trainFilename, 'took', elapsed, 'seconds',\
"%d pct. of timeout" % ((elapsed*100)/timeoutSecs)
print "train parse result:", parseTrainResult['destination_key']
# Parse (test)****************************************
parseTestResult = h2i.import_parse(bucket=bucket,
path=importFolderPath + "/" +
testFilename,
schema='local',
hex_key=testKey,
timeoutSecs=timeoutSecs,
doSummary=False)
elapsed = time.time() - start
print "test parse end on ", testFilename, 'took', elapsed, 'seconds',\
"%d pct. of timeout" % ((elapsed*100)/timeoutSecs)
print "test parse result:", parseTestResult['destination_key']
# GBM (train iterate)****************************************
inspect = h2o_cmd.runInspect(
key=parseTestResult['destination_key'])
paramsDict = define_gbm_params()
for trial in range(3):
# translate it (only really need to do once . out of loop?
h2o_cmd.runInspect(key=parseTrainResult['destination_key'])
### h2o_cmd.runSummary(key=parsTraineResult['destination_key'])
# use this to set any defaults you want if the pick doesn't set
params = {
'response': 54,
'ignored_cols_by_name': 'C1,C2,C3,C4,C5',
'ntrees': 2,
'validation': parseTestResult['destination_key'],
}
h2o_gbm.pickRandGbmParams(paramsDict, params)
print "Using these parameters for GBM: ", params
kwargs = params.copy()
# GBM train****************************************
trainStart = time.time()
gbmTrainResult = h2o_cmd.runGBM(parseResult=parseTrainResult,
timeoutSecs=timeoutSecs,
destination_key=modelKey,
**kwargs)
trainElapsed = time.time() - trainStart
print "GBM training completed in", trainElapsed, "seconds. On dataset: ", trainFilename
gbmTrainView = h2o_cmd.runGBMView(model_key=modelKey)
# errrs from end of list? is that the last tree?
errsLast = gbmTrainView['gbm_model']['errs'][-1]
print "GBM 'errsLast'", errsLast
cm = gbmTrainView['gbm_model']['cms'][-1][
'_arr'] # use the last one
pctWrongTrain = h2o_gbm.pp_cm_summary(cm)
print "Last line of this cm might be NAs, not CM"
print "\nTrain\n==========\n"
print h2o_gbm.pp_cm(cm)
# GBM test****************************************
predictKey = 'Predict.hex'
h2o_cmd.runInspect(key=parseTestResult['destination_key'])
start = time.time()
gbmTestResult = h2o_cmd.runPredict(
data_key=parseTestResult['destination_key'],
model_key=modelKey,
destination_key=predictKey,
timeoutSecs=timeoutSecs)
elapsed = time.time() - start
print "GBM predict completed in", elapsed, "seconds. On dataset: ", testFilename
if DO_PREDICT_CM:
gbmPredictCMResult = h2o.nodes[0].predict_confusion_matrix(
actual=parseTestResult['destination_key'],
vactual='predict',
predict=predictKey,
vpredict='predict', # choices are 7 (now) and 'predict'
)
# errrs from end of list? is that the last tree?
# all we get is cm
cm = gbmPredictCMResult['cm']
# These will move into the h2o_gbm.py
pctWrong = h2o_gbm.pp_cm_summary(cm)
print "Last line of this cm is really NAs, not CM"
print "\nTest\n==========\n"
print h2o_gbm.pp_cm(cm)
# xList.append(ntrees)
if 'max_depth' in params and params['max_depth']:
xList.append(params['max_depth'])
eList.append(pctWrongTrain)
fList.append(trainElapsed)
xLabel = 'max_depth'
eLabel = 'pctWrongTrain'
fLabel = 'trainElapsed'
eListTitle = ""
fListTitle = ""
h2o_gbm.plotLists(xList, xLabel, eListTitle, eList, eLabel,
fListTitle, fList, fLabel)
def test_GBM_params_rand2(self):
h2o.beta_features = False
bucket = 'home-0xdiag-datasets'
modelKey = 'GBMModelKey'
files = [
# ('standard', 'covtype.shuffled.90pct.data', 'covtype.train.hex', 1800, 54, 'covtype.shuffled.10pct.data', 'covtype.test.hex')
('standard', 'covtype.shuffled.10pct.sorted.data', 'covtype.train.hex', 1800, 54, 'covtype.shuffled.10pct.data', 'covtype.test.hex')
]
for (importFolderPath, trainFilename, trainKey, timeoutSecs, response, testFilename, testKey) in files:
h2o.beta_features = False #turn off beta_features
# PARSE train****************************************
start = time.time()
xList = []
eList = []
fList = []
# Parse (train)****************************************
if h2o.beta_features:
print "Parsing to fvec directly! Have to noPoll=true!, and doSummary=False!"
parseTrainResult = h2i.import_parse(bucket=bucket, path=importFolderPath + "/" + trainFilename, schema='local',
hex_key=trainKey, timeoutSecs=timeoutSecs, noPoll=h2o.beta_features, doSummary=False)
# hack
if h2o.beta_features:
h2j.pollWaitJobs(timeoutSecs=timeoutSecs, pollTimeoutSecs=timeoutSecs)
print "Filling in the parseTrainResult['destination_key'] for h2o"
parseTrainResult['destination_key'] = trainKey
elapsed = time.time() - start
print "train parse end on ", trainFilename, 'took', elapsed, 'seconds',\
"%d pct. of timeout" % ((elapsed*100)/timeoutSecs)
print "train parse result:", parseTrainResult['destination_key']
# Parse (test)****************************************
if h2o.beta_features:
print "Parsing to fvec directly! Have to noPoll=true!, and doSummary=False!"
parseTestResult = h2i.import_parse(bucket=bucket, path=importFolderPath + "/" + testFilename, schema='local',
hex_key=testKey, timeoutSecs=timeoutSecs, noPoll=h2o.beta_features, doSummary=False)
# hack
if h2o.beta_features:
h2j.pollWaitJobs(timeoutSecs=timeoutSecs, pollTimeoutSecs=timeoutSecs)
print "Filling in the parseTestResult['destination_key'] for h2o"
parseTestResult['destination_key'] = testKey
elapsed = time.time() - start
print "test parse end on ", testFilename, 'took', elapsed, 'seconds',\
"%d pct. of timeout" % ((elapsed*100)/timeoutSecs)
print "test parse result:", parseTestResult['destination_key']
# GBM (train iterate)****************************************
inspect = h2o_cmd.runInspect(key=parseTestResult['destination_key'])
paramsDict = define_gbm_params()
for trial in range(3):
h2o.beta_features = True
# translate it (only really need to do once . out of loop?
h2o_cmd.runInspect(key=parseTrainResult['destination_key'])
### h2o_cmd.runSummary(key=parsTraineResult['destination_key'])
# use this to set any defaults you want if the pick doesn't set
params = {
'response': 54,
'ignored_cols_by_name':
'0,1,2,3,4',
'ntrees': 2,
'validation': parseTestResult['destination_key'],
}
h2o_gbm.pickRandGbmParams(paramsDict, params)
print "Using these parameters for GBM: ", params
kwargs = params.copy()
# GBM train****************************************
trainStart = time.time()
gbmTrainResult = h2o_cmd.runGBM(parseResult=parseTrainResult,
noPoll=True, timeoutSecs=timeoutSecs, destination_key=modelKey, **kwargs)
# hack
if h2o.beta_features:
h2j.pollWaitJobs(timeoutSecs=timeoutSecs, pollTimeoutSecs=timeoutSecs)
trainElapsed = time.time() - trainStart
print "GBM training completed in", trainElapsed, "seconds. On dataset: ", trainFilename
gbmTrainView = h2o_cmd.runGBMView(model_key=modelKey)
# errrs from end of list? is that the last tree?
errsLast = gbmTrainView['gbm_model']['errs'][-1]
print "GBM 'errsLast'", errsLast
cm = gbmTrainView['gbm_model']['cms'][-1] # use the last one
pctWrongTrain = h2o_gbm.pp_cm_summary(cm);
print "Last line of this cm might be NAs, not CM"
print "\nTrain\n==========\n"
print h2o_gbm.pp_cm(cm)
# GBM test****************************************
predictKey = 'Predict.hex'
h2o_cmd.runInspect(key=parseTestResult['destination_key'])
start = time.time()
gbmTestResult = h2o_cmd.runPredict(
data_key=parseTestResult['destination_key'],
model_key=modelKey,
destination_key=predictKey,
timeoutSecs=timeoutSecs)
# hack
if h2o.beta_features:
h2j.pollWaitJobs(timeoutSecs=timeoutSecs, pollTimeoutSecs=timeoutSecs)
elapsed = time.time() - start
print "GBM predict completed in", elapsed, "seconds. On dataset: ", testFilename
if DO_PREDICT_CM:
gbmPredictCMResult = h2o.nodes[0].predict_confusion_matrix(
actual=parseTestResult['destination_key'],
vactual='predict',
predict=predictKey,
vpredict='predict', # choices are 7 (now) and 'predict'
)
# errrs from end of list? is that the last tree?
# all we get is cm
cm = gbmPredictCMResult['cms'][-1] # use the last one
# These will move into the h2o_gbm.py
pctWrong = h2o_gbm.pp_cm_summary(cm);
print "Last line of this cm is really NAs, not CM"
print "\nTest\n==========\n"
print h2o_gbm.pp_cm(cm)
# xList.append(ntrees)
if 'max_depth' in params and params['max_depth']:
xList.append(params['max_depth'])
eList.append(pctWrongTrain)
fList.append(trainElapsed)
h2o.beta_features = False
xLabel = 'max_depth'
eLabel = 'pctWrongTrain'
fLabel = 'trainElapsed'
eListTitle = ""
fListTitle = ""
h2o_gbm.plotLists(xList, xLabel, eListTitle, eList, eLabel, fListTitle, fList, fLabel)
def test_GBM_params_rand2(self):
h2o.beta_features = False
bucket = 'home-0xdiag-datasets'
modelKey = 'GBMModelKey'
files = [
# ('standard', 'covtype.shuffled.90pct.data', 'covtype.train.hex', 1800, 54, 'covtype.shuffled.10pct.data', 'covtype.test.hex')
('standard', 'covtype.shuffled.10pct.sorted.data', 'covtype.train.hex', 1800, 54, 'covtype.shuffled.10pct.data', 'covtype.test.hex')
]
for (importFolderPath, trainFilename, trainKey, timeoutSecs, response, testFilename, testKey) in files:
h2o.beta_features = False #turn off beta_features
# PARSE train****************************************
start = time.time()
xList = []
eList = []
fList = []
# Parse (train)****************************************
if h2o.beta_features:
print "Parsing to fvec directly! Have to noPoll=true!, and doSummary=False!"
parseTrainResult = h2i.import_parse(bucket=bucket, path=importFolderPath + "/" + trainFilename, schema='local',
hex_key=trainKey, timeoutSecs=timeoutSecs, noPoll=h2o.beta_features, doSummary=False)
# hack
if h2o.beta_features:
h2j.pollWaitJobs(timeoutSecs=timeoutSecs, pollTimeoutSecs=timeoutSecs)
print "Filling in the parseTrainResult['destination_key'] for h2o"
parseTrainResult['destination_key'] = trainKey
elapsed = time.time() - start
print "train parse end on ", trainFilename, 'took', elapsed, 'seconds',\
"%d pct. of timeout" % ((elapsed*100)/timeoutSecs)
print "train parse result:", parseTrainResult['destination_key']
# Parse (test)****************************************
if h2o.beta_features:
print "Parsing to fvec directly! Have to noPoll=true!, and doSummary=False!"
parseTestResult = h2i.import_parse(bucket=bucket, path=importFolderPath + "/" + testFilename, schema='local',
hex_key=testKey, timeoutSecs=timeoutSecs, noPoll=h2o.beta_features, doSummary=False)
# hack
if h2o.beta_features:
h2j.pollWaitJobs(timeoutSecs=timeoutSecs, pollTimeoutSecs=timeoutSecs)
print "Filling in the parseTestResult['destination_key'] for h2o"
parseTestResult['destination_key'] = testKey
elapsed = time.time() - start
print "test parse end on ", testFilename, 'took', elapsed, 'seconds',\
"%d pct. of timeout" % ((elapsed*100)/timeoutSecs)
print "test parse result:", parseTestResult['destination_key']
# GBM (train iterate)****************************************
inspect = h2o_cmd.runInspect(key=parseTestResult['destination_key'])
paramsDict = define_gbm_params()
for trial in range(3):
h2o.beta_features = True
# translate it (only really need to do once . out of loop?
h2o_cmd.runInspect(key=parseTrainResult['destination_key'])
### h2o_cmd.runSummary(key=parsTraineResult['destination_key'])
# use this to set any defaults you want if the pick doesn't set
print "Regression!"
params = {'response': 54, 'ignored_cols_by_name': '5,6,7,8,9', 'ntrees': 2, 'classification': 0}
h2o_gbm.pickRandGbmParams(paramsDict, params)
print "Using these parameters for GBM: ", params
kwargs = params.copy()
# GBM train****************************************
trainStart = time.time()
gbmTrainResult = h2o_cmd.runGBM(parseResult=parseTrainResult,
noPoll=True, timeoutSecs=timeoutSecs, destination_key=modelKey, **kwargs)
# hack
if h2o.beta_features:
h2j.pollWaitJobs(timeoutSecs=timeoutSecs, pollTimeoutSecs=timeoutSecs)
trainElapsed = time.time() - trainStart
print "GBM training completed in", trainElapsed, "seconds. On dataset: ", trainFilename
gbmTrainView = h2o_cmd.runGBMView(model_key=modelKey)
# errrs from end of list? is that the last tree?
errsLast = gbmTrainView['gbm_model']['errs'][-1]
print "GBM 'errsLast'", errsLast
cm = gbmTrainView['gbm_model']['cm']
pctWrongTrain = h2o_gbm.pp_cm_summary(cm);
print "Last line of this cm might be NAs, not CM"
print "\nTrain\n==========\n"
print h2o_gbm.pp_cm(cm)
# GBM test****************************************
predictKey = 'Predict.hex'
h2o_cmd.runInspect(key=parseTestResult['destination_key'])
start = time.time()
gbmTestResult = h2o_cmd.runPredict(
data_key=parseTestResult['destination_key'],
model_key=modelKey,
destination_key=predictKey,
timeoutSecs=timeoutSecs)
# hack
if h2o.beta_features:
h2j.pollWaitJobs(timeoutSecs=timeoutSecs, pollTimeoutSecs=timeoutSecs)
elapsed = time.time() - start
print "GBM predict completed in", elapsed, "seconds. On dataset: ", testFilename
print "FIX! where do we get the summary info on the test data after predict?"
h2o.beta_features = False
